OneThinker: All-in-one Reasoning Model for Image and Video

A new study introduces a framework called UNIFIER, aimed at addressing catastrophic forgetting in Multimodal Large Language Models (MLLMs) during continual learning in visual understanding. The research constructs a multimodal visual understanding dataset (MSVQA) that includes diverse scenarios such as high altitude and underwater perspectives, enabling MLLMs to adapt effectively to dynamic visual tasks.

A new method called Contextual Image Attack (CIA) has been proposed to exploit safety vulnerabilities in Multimodal Large Language Models (MLLMs) by embedding harmful queries within benign visual contexts. This approach utilizes a multi-agent system and four visualization strategies to enhance the attack's effectiveness, achieving high toxicity scores against models like GPT-4o and Qwen2.5-VL-72B.

A new framework named MLLMsent has been proposed to enhance the sentiment reasoning capabilities of Multimodal Large Language Models (MLLMs). This framework explores sentiment classification directly from images, sentiment analysis on generated image descriptions, and fine-tuning LLMs on sentiment-labeled descriptions, achieving state-of-the-art results in recent benchmarks.